Validation of dentine deposition rates in beluga whales by interspecies cross dating of temporal δ13C trends in teeth
Bibliographic record
Abstract
Isotopic time series from sequentially sampled growth layer groups (GLGs) in marine mammal teeth can be combined to build chronologies allowing assessment of isotopic variation in marine ecosystems. Synchronous recording of baseline isotopic variation across dentinal GLGs of species with temporal and spatial overlap in foraging offers a unique opportunity for validation of marine mammal age estimation procedures through calibration of GLG deposition rates in one species against another whose GLG deposition has been independently determined. In this study, we compare trends in stable carbon isotope ratios (d13C) across dentinal GLGs of three eastern Canadian Arctic (ECA) beluga (Delphinapterus leucas) populations through the 1960s-2000s with a d13C time series measured across dentinal GLGs of ECA/Northwest Atlantic killer whales (Orcinus orca) from 1944-1999. We use confirmed annual GLG deposition in killer whales as a means to assess beluga GLG deposition, and show linear d13C declines across chronologies of both species were statistically indistinguishable when based on annual GLG deposition in beluga whales, but differed when based on biannual deposition. We suggest d13C declines reflect the oceanic 13C Suess effect, and provide additional support for annual GLG deposition in beluga whales by comparing rates of d13C declines across beluga GLGs with published annual d13C declines attributed to the oceanic 13C Suess effect in the North Atlantic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".